A Course in Machine Learning

The purpose of this book is to provide a gentle and organized introduction to the field. This is in contrast to most existing machine learning texts, which tend to organize things topically, rather than conceptually. This makes sense for researchers in the field, but less sense for learners.

The second goal of this book is to provide a view of machine learning that focuses on ideas and models, not on math. It is not possible or even advisable to avoid math. However, mathematics should be there to aid understanding, not hinder it.

Finally, this book attempts to have minimal dependencies, so that one can fairly easily pick and choose chapters to read. When dependencies exist, they are listed at the start of the chapter, as well as the list of dependencies at the end of the chapters.

The audience of this book is anyone who knows differential calculus and discrete math and can program reasonably well. A little bit of linear algebra and probability will not hurt.

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